Learning modular object-centric representations is crucial for systematic
generalization. Existing methods show promising object-binding capabilities
empirically, but theoretical identifiability guarantees remain relatively
underdeveloped. Understanding when object-centric representations can
theoretically be identified is crucial for scaling slot-based methods to
high-dimensional images with correctness guarantees. To that end, we propose a
probabilistic slot-attention algorithm that imposes an aggregate mixture prior
over object-centric slot representations, thereby providing slot
identifiability guarantees without supervision, up to an equivalence relation.
We provide empirical verification of our theoretical identifiability result
using both simple 2-dimensional data and high-resolution imaging datasets.

学习模块化的物体中心表示对于系统化的泛化至关重要。现有的方法在经验上显示出有前景的物体绑定能力，但理论上的可识别性保证相对较少。理解何时可以在理论上识别物体中心表示对于按槽位进行高维图像的扩展方法具有重要意义且具有正确性保证。为此，我们提出了一种概率化的槽位注意力算法，通过在物体中心化槽位表示上施加聚合混合先验，从而在没有监督的情况下提供槽位可识别性保证，达到等价关系。我们通过简单的二维数据和高分辨率成像数据集进行了我们的理论可识别性结果的实证验证。

基于概率槽注意力的可识别物体中心表示学习

Identifiable Object-Centric Representation Learning via Probabilistic  Slot Attention

Causal representation learning has showed a variety of settings in which we
can disentangle latent variables with identifiability guarantees (up to some
reasonable equivalence class). Common to all of these approaches is the
assumption that (1) the latent variables are represented as $d$-dimensional
vectors, and (2) that the observations are the output of some injective
generative function of these latent variables. While these assumptions appear
benign, we show that when the observations are of multiple objects, the
generative function is no longer injective and disentanglement fails in
practice. We can address this failure by combining recent developments in
object-centric learning and causal representation learning. By modifying the
Slot Attention architecture arXiv:2006.15055, we develop an object-centric
architecture that leverages weak supervision from sparse perturbations to
disentangle each object's properties. This approach is more data-efficient in
the sense that it requires significantly fewer perturbations than a comparable
approach that encodes to a Euclidean space and we show that this approach
successfully disentangles the properties of a set of objects in a series of
simple image-based disentanglement experiments.

利用因果表征学习和面向物体的学习相结合，通过修改 Slot Attention 架构，开发出了一种利用稀疏扰动进行弱监督的物体中心化架构，以更少的扰动成功解缠多个物体的属性。